Demand Forecasting Reporting Period: 1 st Apr th Jun 17

Transcription

1 N A T I O N A L G R I D P A G E 1 O F 3 C O M M E R C I A L, E L E C T R I C I T Y C O M M E R C I A L O P E R A T I O N S Demand Forecasting Reporting Period: 1 st Apr th Jun 17 EXECUTIVE SUMMARY This report provides an overview of causes for demand forecast errors, and the work being done by National Grid to reduce these errors. It also provides detailed statistics on the errors in forecasts produced over the preceding year for demands in the reporting period of 1 st April 217 to 18 th June 217. This is the first of a series of new reports and so we have provided historical and relevant context. Future reports will be shorter and restricted to the three months. INTRODUCTION Under section 4H of the Transmission Licence, National Grid is obliged to publish national system demand forecasts, wind and solar generation forecasts to the market participants. This quarterly report contains information about demands, solar & wind generation output we have experienced within the reporting period 1. During this period we experienced second lowest demands for both overnight and day time minimums since 25. We have also experienced record PV generation output. It is also the first time when the day time minimum (3B) demand was lower than the overnight minimum (1B), on 9 th April 217. For the half hour ending 15 hrs. 3B demand was 2.3 GW and 1B demand was 2.5 GW for the half hour ending 5 hrs. Key summary statistics of the reporting period are given in the table below. Key Numbers Date Half hour ending Peak demand 37.9 GW 5 th Apr Overnight minimum 16.5 GW 11 th Jun 17 6 Daytime minimum 2.3 GW 9 th Apr Maximum PV Output 8.9 GW 26 th May Maximum Metered Wind GW 6 th Jun Maximum Emb Wind Output 3.1 GW 6 th Jun Reporting period 1 st April th June Based on initial operational metering available on the BMRS website 1

2 At the end of the period, an installed generation capacity of wind and solar generation was: Generation Type Metered Wind Embedded Wind Embedded Solar Total Wind & Solar Capacity 1.4 GW 5.1 GW 12. GW 27.5 GW DEMAND FORECAST ERRORS The table below shows the mean absolute error by cardinal points by forecast horizon, from within day to 13 days ahead. The mean absolute error is the error statistic used to define the performance targets under the forecasting incentive scheme, and so is used in this report to describe errors. As would be expected, errors were lower closer to real time, and were larger during the sunnier times of the day when uncertainties in PV generation have the greatest impact on our demand forecast errors. CPID In Day DA 2DA 3DA 4DA 5DA 6DA 7DA 8DA 9DA 1DA 11DA 12DA 13DA 1F S A B F A B B C A B C Demand forecasts are built up of several components, each of which introduces its own errors. The models start from a basic demand level, determined from longer term trends such as changes in GDP or trends in energy use. There are a series of time based corrections, such as differences in demand patterns at weekends. There are then a series of weather based corrections, such as the impact of temperature on demand. We forecast the demand required to be met from the National Transmission system, and not the total electricity consumption of the country. This means that the effect of embedded or distributed generation not connected to the Transmission system is to suppress the demand required from the Transmission system. Thus, in order to forecast this demand we have to forecast the behaviour of embedded generation, in particular weather variable wind and solar. We create forecasts for embedded wind and solar, and use these to correct the demand forecast. Finally, we add corrections for special events that may impact on demand. Post event we can measure the actual demand, and thus calculate an overall error. We can then seek to apportion this error to the components building up the demand. Some of these can be done with more accuracy than others. These components are discussed in more detail in Appendix 1. 2

3 For the purpose of the incentivised forecasts, outturn demand is measured as the sum of generation from operational metering supplied to the National Grid Electricity National Control Centre in real time from power stations connected to the transmission system. It is possible for meters to break or produce erroneous data, introducing another source of error in calculating the forecast error. We produce forecasts at multiple time horizons, from within day up to five years ahead. Overall we would typically produce over 1 forecasts for the peak demand on a particular day over the preceding five years. Forecasts at greater than 14 days ahead are based on seasonal average weather, while those from within day to 13 days ahead use the latest available weather forecasts. It should be noted that most weather forecast models run from to 6 days ahead, so the accuracy of weather forecasts declines significantly from 7 to 13 days ahead. The graphs below show how mean absolute error in cardinal point forecasts change with forecast horizon from to 13 days ahead. The data is broken into forecasts for daytime, evening and overnight cardinal points. The data shows that errors increase at greater time horizons, and that daytime errors are significantly higher than those for late evening and overnight when solar generation is not an issue. 3

4 INCEN T IVISED D EMAND FOR EC AST S In this section of the report we present data and graphs for the incentivised demand forecasts, i.e. day ahead, 2 day ahead and 7 day ahead. Since the beginning of the incentive year, we have successfully reduced our mean absolute errors for each forecast horizon. The graph below depicts weekly MAE for each of the timescale. We have also been successful in driving down the number of large errors (<1 MW). It should be noted that the data for June only covers 18 days, and so a lower number of errors would be expected. 4

5 2-52 W EEKS AH EAD DEMAND FO REC ASTS The graph below shows our 2-52 weeks ahead peak demand forecasts published in June 216 on the BMRS website for the reporting period 1 st Apr th June 17. As noted above, these forecasts are based on seasonal average weather, and so we present weather corrected results as well as the raw actual demands. In general there is fairly good agreement between the weather corrected demands and the forecasts made a year earlier. The mean absolute errors for 2-52 weeks ahead time scale peak demand forecasts for both actual and weather corrected demands are depicted below. For the reporting period as a whole the average mean absolute error was 1.5 GW, with a maximum error of 5. GW on the actual demand basis and 3.3 GW on the weather corrected basis. 5

6 EMBEDDED PV PV capacity has increased from 1.5 GW in June 16 to 12. GW in June 17 D ESCRIPT ION O F SOLAR G EN ER ATION AND F OR EC AST ERRORS Our solar generation forecasts, as published on BMRS and the Data Explorer section of the National Grid website, arise from an internal PV generation forecasting model, based on a number of parameters:- Estimated capacities, Weather forecasts from our weather provider at 28 geographical locations and Empirically derived models connecting radiation and national generation using data from our collaboration with Sheffield Solar. Our weather forecasts extend from the current day out to 14 days ahead, and so our solar generation forecasts have the same time horizon. Beyond this, we use seasonal normal weather. The figure below gives an illustration of our mean absolute solar generation forecast error for daylight hours, against lead time of the forecast in days ahead (DA). The error is measured against the national generation estimated from our Sheffield Solar NIA project. Some further details on the trends are given in the analysis section later in this chapter. 6

7 Mean abs PV Error(MW) 13DA - 88 MW 12DA MW 11DA MW 1DA MW 9DA - 86 MW 8DA MW 7DA MW 6DA MW 5DA MW 4DA MW 3DA MW 2DA MW DA MW ACTION S AF FECTING SOLAR G EN ER AT IO N F OR EC AST S AND ERRORS During the period 1 st April 18 th June, a number of actions had been taken to optimise the solar generation forecast:- Updated PV generation models o As part of our regular PV model reviews, we refreshed the PV generation models on the 5 th of May leading to an immediate improvement of around 2MW in mean absolute error for D+1 and D+2. These improvements generally reduce with time until the next refresh. Improved solar radiation forecasts o Following completion of one the work packages of our NIA collaboration with the Met Office, on the 5 th of May we were able to implement the results of the work into our operational feed. Radiation is notoriously difficult to forecast, yet remains one of the main sources of our solar generation errors. The new radiation forecast showed a reduction of around 5MW in DA and 2DA mean absolute errors.. 7

8 ANAL YSIS O F TH E C AU SES O F SOL AR G ENERAT ION F OR EC AST ERRORS & FU TURE ACTION S As a number of parameters feed into the forecast of solar generation, errors in each of these contribute to the discrepancy with the actual real generation. However, as error in capacity or outturn can only be estimated theoretically (as there are no standard benchmarks or metering available to National Grid), we take the estimates of capacity and outturn as exact, and break our forecast error down into just two components Model Error and Weather Error:- Error = P (weather forecast) Observed Outturn = (P (weather actual) - Observation) + (P (weather forecast) P (weather actual)) Model Error + Weather Error where P is the function being used to convert weather data into solar generation data. Radiation forecast is the main weather variable used from our weather forecasts. The figure below illustrates the values of mean radiation forecast error seen during the periods 1 st April 18 th June:- 8

9 We know from sensitivity analysis of our internal models, that an error of 5kJ/m2 in radiation is equivalent to around a 15MW error in our national PV forecast. Using this scaling to cross compare the values in the above figure to those in the figure of mean absolute PV error, highlights that radiation or weather error has been the biggest contributor to our overall error during this reporting period. As stated earlier, radiation is notoriously difficult to forecast principally because of the unconstrained size and shape of cloud, yet the minimum grid sizes of leading numerical weather prediction models is around 1.5km.. Temporally, 7days ahead is also known to be about the limit of forecasting skill for solar radiation, beyond which forecasts typically reflect seasonal patterns rather than current weather dynamics. Nevertheless, we are continuing to work in tandem with our weather provider via the NIA to bring these error values down. Other immediate work is centred around improvements to our PV generation model errors. A first step will be the use of an additional set of 25 weather forecast stations in our models, which we expect to lead to a more accurate forecasts in the aggregated national value. Finally, we are working with both the Turing Institute this summer, as well as Reading University, through NIA collaborations looking at more advanced PV generation models.. Through these collaborations we expect to be able to capture a wider range of significant variables such as angle of the sun, panel efficiency for example, which upon implementation should improve our forecast accuracy further. 9

10 WIND FORECASTS The wind forecast incentive requires us to publish a half hourly wind generation forecast. We currently receive wind speed forecasts at hourly resolution, and so we have developed an interpolation algorithm to estimate values for the half hours. This is one main source of error, and this will be tuned over the next few months. As a common feature of wind speed forecasts is for a weather forecast to correctly forecast a front moving but to have an hour or two error in the timing of the front. Half hourly generation forecasts increase the impact of this issue in causing errors where the forecast shape is correct, but displaced in time by one or two settlement periods. We also implemented a new bias correction algorithm in the middle of April which improved the forecasts after this date. Again this new algorithm will continue to be improved over the coming months. The key input to our forecasts is the wind speed forecast. The graph below shows the mean absolute error in wind speed forecast in m/s at different time horizons. As an indication of the impact of the errors, in the mid part of a wind power curve, between around 5 and 15 m/s, a 1 m/s error in wind speed forecast equates to around a 1% error in wind power forecast for the generator. Mean abs wind speed error (m/s) 13DA m/s 12DA m/s 11DA m/s 1DA m/s 9DA m/s 8DA m/s 7DA m/s 6DA m/s 5DA m/s 4DA m/s 3DA m/s 2DA m/s DA -.95 m/s In Day -.61 m/s 1

11 The wind incentive measures our performance in forecasting the wind power that is available, and does not require us to forecast the actual wind output after the control room have taken BOAs on wind farms to manage constraints or other system issues in real time. In order to report this, the forecasts for each individual wind farm for each half hour are recorded. For and half hour where a BOA is taken on that farm, the unit is discounted from the calculation of corrected forecast, corrected outturn and corrected installed capacity. The percentage error for the forecast for that half hour is then calculated from the corrected values, and a daily average then calculated which is used in the formulae described in the incentive to calculate the outcome for that day. In terms of overall performance, the graphs below show the comparison of corrected forecast and actual wind generation for April and May, as well as the daily average percentage error used to calculate the outcome of the incentive. 9 Forecast and Actual Wind Generation (BOA Corrected) Apr Apr 2-Apr 3-Apr 4-Apr 5-Apr 6-Apr 7-Apr 8-Apr 9-Apr 1-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 2-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 3-Apr Forecast Actual 11

12 16 Daily Percentage Forecast Accuracy Compared with Target Apr 2-Apr 3-Apr 4-Apr 5-Apr 6-Apr 7-Apr 8-Apr 9-Apr 1-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 2-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 3-Apr 9 Forecast and Actual Wind Generation (BOA Corrected) Apr Apr 2-Apr 3-Apr 4-Apr 5-Apr 6-Apr 7-Apr 8-Apr 9-Apr 1-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 2-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 3-Apr Forecast Actual 12

13 16 Daily Percentage Forecast Accuracy Compared with Target May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 1-May 11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 2-May 21-May 22-May 23-May 24-May 25-May 26-May 27-May 28-May 29-May 3-May 31-May The other component of the incentive is the bias incentive. In this component, for the objective is to have as many over-forecasts as under-forecasts, with a target of 6:4 and a floor of 7:3. There is also a separate term in the incentive that says if any two individual forecast times are 7% biased then we hit the floor. For the wind incentive, this means if any two out of 48 half hourly forecasts are 7% biased then we hit the floor. This equates to 9 out of 31 forecasts in a month being one side of zero and 22 being the other side. In April the forecasts overall were 72.5% biased and so we hit the floor. However, in May, the overall bias was 54.5%, which equates to a 11.6k profit. However, forecasts for 1 and 1:3 were both split with 22 over-forecasts and 9 under-forecasts. Hence we incurred the maximum loss for this month. It is interesting to note that the forecast for 1 on 23 rd May, at 1973 MW, was 18 MW overforecast. Had the forecast been 1954 MW we would have under-forecast and so received 11.6k on this component of the incentive rather than losing 2.8k. 13

14 WEATHER ANALYSIS The graphs below show the mean absolute errors in the forecasts we receive for our key weather parameters over the to 13 day ahead forecast horizons. Temperature is measured in C, with 1 C equating to a demand forecast error of the order of 5 MW. Solar radiation is measured in kj/m 2, with 5 kj/m 2 approximating to an error of 15 MW in our PV forecasts Wind speed is measured in m/s, with 1 m/s in the range 5 15 m/s corresponding to an error in generation forecast for the wind farm of around 1% of capacity Illumination Deficit is a calculated parameter used to estimate the effect of dull or dark days on increased lighting load, with a change in Illumination Deficit of 1 approximating to a demand change of 2 MW. Error In Temperature Forecasts At Different Forecast Horizons Error In Radiation Forecasts At Different Forecast Horizons Mean Error Max Error 98 Percentile Mean Error MaxError 98Percentile Error In Wind Speed Forecasts At Different Forecast Horizons Error In Illumination Forecasts At Different Forecast Horizons Mean Error Max Error 98 Percentile Mean Error MaxError 98Percentile 14

15 The scale of the maximum errors, particularly at 7 days ahead, demonstrate the difficulty in producing accurate demand forecasts at this time horizon where significant weather forecast errors are possible. 8 C temperature error equates to around 4 GW demand forecast error. The weather forecast errors are not independent, and so to fully understand the impact of weather forecast error on demand forecast error it is necessary to consider all the forecast errors together. Looking at the 3C afternoon peak, we can see average errors at 7 days ahead in excess of 2 GW with maximum errors approaching 1 GW. 12 Average, Minimum and Maximum Margin of Error Over 13 Days Forecast (3C) 1 Margin of Error (MW) Average Maximum Minimum Number of Days Forecast (Days) 15

16 The graphs below show the frequency of occurrence of different magnitude errors over the key forecast time horizons. Frequency of Error Frequency of Error 1DA (3C) More Error Range (MW) Frequency of Error 2DA (3C) Frequency of Error Error Range (MW) 16

17 Frequency of Error Frequency of Error 7DA (3C) More Error Range (MW) The following graphs show a similar analysis of the effect of weather forecast error on the 1B overnight minimum. As would be expected, there is less sensitivity to weather error, but it is still significant. It should be noted that this analysis looks at the impact of weather forecast errors on the weather models in our main demand forecasts. The impact on embedded PV and wind generation models is not included in these graphs. 6 Average, Minimum and Maximum Margin of Error Over 13 Days Forecast (1B) 5 Margin of Error (MW) Average Maximum Minimum Number of Days Forecast (Days) 17

18 Frequency of Error Forecasting Error 7DA 1DA (1B) (1B) More Error Range (MW) Frequency 25 Forecasting Error 2DA (1B) Frequency of Error Frequency Error Range (MW) Forecasting Error 7DA (1B) Frequency of Error Frequency More Error Range (MW) 18

19 WORK TO IMPROVE FORECAST ACCURACY We forecast demand on the transmission system. Embedded generation has the effect of suppressing demand. Consequently in order to accurately forecast demand we have to forecast the behaviour of embedded generation, particularly weather variable embedded generation, PV and wind, that can vary significantly day to day. A review of our mean absolute error over the last ten years shows a significant increase in daytime errors that correlates strongly with the growth in PV capacity, with a 6% increase in average error in the last three years compared with the long term trend.. A similar review of the errors in our forecasts for overnight minimum, where PV is not a factor, shows a growth over the last three years of 2%. This growth correlates to the growth in embedded wind and other non weather dependent embedded generation. The growth in overnight errors is around 1/3 of the growth in daytime errors, and so we have focused 75% of our efforts on improving our PV forecasts and 25% on other embedded generation. This is shown in the graph below. Installed PV Capacity vs Mean Absolute Error Apr-25 Aug-25 Dec-25 Apr-26 Aug-26 Dec-26 Apr-27 Aug-27 Dec-27 Apr-28 Aug-28 Dec-28 Apr-29 Aug-29 Dec-29 Apr-21 Aug-21 Dec-21 Apr-211 Aug-211 Dec-211 Apr-212 Aug-212 Dec-212 Apr-213 Aug-213 Dec-213 Apr-214 Aug-214 Dec-214 Apr-215 Aug-215 Dec-215 Apr-216 PV Capacity Daytime Peak Overnight Minimum It is noted that the demand forecasting incentive targets are set as the average errors for the last three years, and so do not take account of the impact of the growth of installed PV capacity. As a consequence of this, significant work has to be done to offset the impact of PV just to return the forecast error to the average position and so break even on the incentive. 19

20 PV MO D ELLIN G PV forecasting has four components: the input solar radiation forecasts from weather forecasting companies; the capacity of installed generation; the models used to convert the radiation forecasts into generation forecasts; and the systems used to create the forecasts. We have been active in all these areas. The main drive from was on upgrading EFS to deal with PV models, and the additional weather forecast data necessary as inputs to these models. These changes were under the TARMap project. As a result of this work we moved from a spreadsheet using solar radiation forecasts for seven locations for four points in the day for five days ahead to an IS supported PV tool integrated into the main Energy Forecasting System, receiving forecasts for solar radiation for 26 geographic locations for every hour of the day for 14 days ahead. In addition to the system changes we had to change our contracts with our weather forecast provider to provide the additional data. From 215 to early 217 the two areas of work were on obtaining data to allow us to develop high quality models and in improving the quality of the solar radiation forecasts. On the data side, the top priority was to understand the volume of PV generation. A 3k project was initiated, funded from Ofgem's Network Innovation Allowance with Sheffield Solar to get an estimate for national PV generation for each settlement period. This project uses Bayesian Response Surfaces to extrapolate data from a few hundred live measurements into an estimated total from all 9, PV installations in the country. In order to provide the data necessary for this project, a 1k contract was negotiated with PassivSystems which delivers live PV output from around 12 domestic PV installations, as well as data from 2, installations at day + 1. In the last two months this data has been integrated with the Sheffield Solar projects tools in order to provide a live estimate of PV national output accurate to around 3%. In addition the Online Modelling team have taken the data via a standalone interface and used it to provide a live display in the Control Room of PV generation round the country. The team are still working on fully integrating the data feed into Data Historian. This reliable generation data has allowed us to considerably improve the quality of the PV models used to convert the weather forecasts to generation forecasts. In parallel with this work, a 4k NIA project was initiated with the Met Office in late 215 aimed at improving the quality of the PV forecasts. This project falls into four work packages. The first package was delivered two months ago. This replaces the forecast based on a single Met Office weather model with a blend of multiple weather forecasts. This has had the effect of reducing the mean absolute error in the generation forecast by around 115 MW, as well as significantly reducing the instance of large PV errors. The next phase of the work is to deliver a further numerical filtering of the forecasts to remove systematic bias, with a further phase of delivering hourly updates to the Met Office solar radiation forecasts (something they currently cannot do) and a final parallel phase looking at improving the Met Office's understanding of the physics of clouds. 2

21 We have also procured for around 2k a separate weather visualisation system including a PV forecast model from Met Desk. This tool updates hourly with new weather forecasts. This tool was initially used in standalone mode to help the forecasters judge the reliability of the Met Office forecasts and decide whether to apply a manual correction to the demand forecasts. The Online Modelling team has now integrated this data feed with the Sheffield Solar output and our own forecasts in order to provide the control room and forecasters with a tool showing how both forecasts are performing each day. The next step is to further improve the quality of the PV models, using the PassivSystems data. This week we have started a three month NIA project with the Turing Institute aimed at delivering improved PV and wind models, based on statistical analysis of the data. We are also a year into a 3k NIA project with Reading University which will deliver an advanced physics based model of PV generation. Our systems are developed to allow us to use both the statistical and physical models and to select the one which performs best. In recent weeks we have also gone live with a change to the times when the Met Office weather forecasts are received into our forecasting systems. This required work with the Met Office, as well as changes to the forecasting system. We have advanced the daytime forecasts by an hour, meaning that we are now able to provide the control room with a more up to date forecast in time for them to position plant for the afternoon, and also allows us to produce a two day ahead forecast for the electricity market based on more up to date weather forecasts. We are currently also working with Sheffield Solar in developing an estimated output at Grid Supply Point level as an extension to their NIA project. This is due to deliver in October. In parallel with this we have been working on agreeing with our IS partners the changes necessary to EFS to allow our GSP level forecasts to take account of local PV forecasts as well as local embedded wind forecasts. This data will be useful to EBS in calculating local constraints. The impact of these developments can be seen from considering the error in our daytime forecasts as a fraction of installed PV capacity. The graph below shows that the forecast error for the 3B cardinal point has fallen from an average of 91 MW per GW of installed PV capacity in the year up to May 16, to an average of 77.5 MW per GW of installed PV capacity over the last 12 months, a 15% reduction in error. 21

22 92. Rolling 365 Day Mean Absolute Error for 3B normalised by PV Capacity Shown as average MW error per GW of PV Capacity W IND GENERATION MO DELLING Our focus over the last few years has been on PV rather than wind. Last year we refreshed all our models for larger wind farms, using a cubic spline technique added to EFS in the April 15 TARMap changes. This allows a more accurate statistical fit of the power curve to the available data. In particular this improves the models at high wind speeds when generators shut down. We are currently in the process of reviewing the curves. In addition the Turing project includes work on wind models, and we are looking to take account of wind direction in the models, as well as developing methodologies for automating the updates of these models. O TH ER EMBEDDED GENER AT ION MOD ELLIN G Last year in collaboration with the Energy Insights team we obtained from ElectraLink a historic dataset of four years of embedded generation, aggregated by fuel type and by GSP at settlement period resolution. Two weeks ago we started a 1k NIA project with the Smith Institute aimed at providing models of this embedded non weather variable generation. This work is due to deliver in October, and is planned to be incorporated into EFS along with the embedded PV at GSP level discussed above. 22

23 The initial NIA proposal developed with the Smith Institute included a work package to look at modelling energy storage. This was put on hold pending a structured approach to energy storage being produced by a series of workshops being run by the Innovation team. CONCLUSION A detailed analysis is presented showing the sources of error in National Grid s demand forecasts for the period 1 st April 217 to 18 th June 217. In addition, the work that is being done to improve these forecasts is described. The analysis notes how the nature of the errors impacts on the demand forecasting incentives performance, in particular the effect of increased PV capacity not being allowed for in the accuracy targets, and the impact that one or two very small errors can have on the bias incentives. 23

24 APPENDIX 1 SOURCES OF DEMAND FORECAST ERROR Forecast error is made up of many independent components. On some days small errors due to several different components can result in a large overall error, while on other days two very large errors can cancel out leaving a net very accurate forecast. The components of error are: Error in basic underlying demand level cannot be directly quantified on a day to day basis Error in time data some components such as day of week do not have any errors while other components such as the number of schools on holiday each week will have an error that cannot be quantified Error in time models eg our representation of how day of week affects demand, or how the number of schools on holiday affects demand cannot be separately quantified Error in weather forecast data difference between forecast and actual observed weather at weather stations can be measured, and our weather models can be used to estimate the impact of these errors on our demand forecasts Error in weather models the error in how well we model the effects of weather on demand cannot be independently measured and so cannot be quantified Error in wind and PV capacity our embedded wind and solar models assume a capacity of generation, and its geographical location. National Grid has limited data on such installations; some are notified to us while data on others has to be gathered from publically available data sources. This data will not be exhaustive and so is a source of error, particularly in areas of rapid growth such as PV installations where there can be some months delay between installations going live and data being available to indicate the existence of the installation. This error cannot be quantified. Error in solar radiation forecasts the solar radiation forecasts are a key input to our forecasts for PV generation, and can be a significant source of errors. One source of error is in the solar radiation forecast we receive for a specific weather station location. The error can be quantified by comparing forecast and measured solar radiation at the location. The second error is in how representative the weather station is of the surrounding area. It is possible for a cloud to be correctly forecast over the weather station, but for the majority of the surrounding area to be in sunshine. We currently receive solar radiation forecasts for 26 locations round the country, meaning a 1 cm 2 radiation sensor at each weather site is assumed to be representative of an average of 884 km 2. The error this introduces cannot be quantified. Error in PV models we use models to convert forecast solar radiation into forecast PV generation in MW. There will be an error due to the accuracy of these models. This error can be estimated using the output of a Network Innovation Allowance funded project run by National Grid in collaboration with the Sheffield Solar team of Sheffield University. This team take around 12 real time data feeds from domestic PV installations and use complex algorithms to extrapolate the output to an estimate for the total national output from all 9, or so PV installations in GB. There is an estimated 3% error in the value of PV generation. We can compare the estimated national PV generation with the value that our models would have forecast given the outturn solar radiation and so estimate the error associated with the PV models. 24

25 Error in wind speed forecasts we currently receive wind speed forecasts for around 1 locations in the country. Some of these are weather station locations where an outturn measurement is available, allowing us to quantify the error at these locations, but other forecasts are for sites near large wind farms where outturn data may not be available. We can however estimate the error from those points that we can measure. Error in embedded wind generation models we use models to convert the nearest available wind speed forecast into a generation forecast for embedded wind farms that we are aware of. As we do not have any metering from these embedded wind farms we cannot measure this error, although we can estimate it is likely to be broadly similar to the errors in our national metered wind generation forecast. Error due to system frequency deviation from 5 Hz our models assume the system is operating at 5 Hz. In reality is it possible for the average frequency over a half hour settlement period to vary from 5 Hz, typically by up to.1 Hz once a week. This introduces a change in demand from that forecast for 5 Hz. The change is in the range 7 14 MW for a.1 Hz deviation, and can be estimated based on frequency measurements. Error due to transmission losses our demand forecasts are for National Demand, which includes transmission losses. We assume typical losses in our forecasts, however it is possible for operational issues on the day to require an abnormal system configuration which could result in higher than normal losses. It is possible for a difference of over 5 MW in transmission losses in consecutive days for the same settlement period. This error can be quantified from Settlement Metering values for generation and demand that are available via Elexon from at Day + 5. The separate components of the demand forecast, and associated error, are represented visually in the illustration below. 25

26 APPENDIX 2 BREAKDOWN OF LARGE INDIVIDUAL ERRORS Day Ahead largest forecasting errors Date CP Forecast Actual Error Forecaster Note 14 th Apr 17 4A Easter Bank Holiday Weekend, duller than forecast weather resulted under forecasting of demand, -5 MW PV Error 17 th Apr 17 3B Easter Bank Holiday Weekend, Solar radiation was lower than forecast resulting duller than forecast weather conditions, - 8 MW PV error. Weather was less windy than forecast too. 6 th Jun 17 2A During the 2A time frame, it was much duller than forecast. Solar radiation 1 hrs was 529 kj/m2 compared to forecast 15 kj/m MW PV error 14 th Apr 17 3B Easter Bank Holiday Weekend, duller than forecast weather resulted under forecasting of demand, -11 MW PV Error 2 nd May 17 3B May Bank Holiday - Weather was slightly colder than forecast and less winder than forecast. It was duller than forecast 1 MW PV Error 2Days Ahead largest forecasting errors Date CP Forecast Actual Error Forecaster Note 6 th Apr 17 3B PV Outturn was 17 MW higher than forecast. Solar radiation outturn nationally was : compared to forecast 1544 kj/m2. Temp was 1. o C milder than forecast. 25 th Apr 17 3B Solar radiation outturn nationally was : compared to forecast 2333 kj/m2. 15 MW PV forecast error over 3B cardinal point 6 th Apr 17 2B PV Outturn was 14 MW higher than forecast Solar radiation outturn nationally was : compared to forecast 195 kj/m2 Temp was.5 o C milder than forecast. 3 rd May 17 3C MW PV Error at 18:hrs Solar radiation outturn nationally was :hrs compared to forecast 17 kj/m2. 6 th Apr 17 3C Solar radiation outturn nationally was :hrs compared to forecast 552 kj/m2 26

27 7Days Ahead largest forecasting errors Brighter than forecast weather conditions resulted over forecasting error. Temp was 1.2 o C milder than forecast. Date CP Forecast Actual Error Forecaster Note 9th Apr 17 3B 4A Weather was totally out on the day compared to forecast done 7days ago. It 2B was up to 5.5 o C warmer, twice as brighter and winder than forecast resulting demand over forecast Solar radiation outturn nationally was :hrs (3B) compared to forecast 1467 kj/m2. PV Error 1 MW. Solar radiation outturn nationally was :hrs (2B) compared to forecast 959 kj/m2. PV Error 3 MW. Temperature outturn nationally was 17.4 o 15:hrs (3B) compared to forecast 11.8 o C. Temperature outturn nationally was 17.5 o 18:hrs (4A) compared to forecast 11.9 o C. 5. o C warmer than forecast temperature contributing almost 2.5 GW to demand error. Wind speed outturn nationally was At 3B - 12 m/s compared to forecast 8m/s. At 4A - 14 m/s compared to forecast 7m/s. 11 th Apr 17 3B Solar radiation outturn nationally was :hrs (3B) compared to forecast 1286 kj/m2. PV Error 2 MW. Temperature outturn nationally was 13.6 o 15:hrs (3B) compared to forecast 11.3 o C. 6 th May 17 3C Weather was duller than forecast. Resulting large illumination error. Solar radiation outturn nationally was :hrs (3C) compared to forecast 1146 kj/m2. PV Error 2 MW. 27

28 APPENDIX 3 DETAILED ERROR ANALYSIS 28

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30 3